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Title Biostatistics II: Logistic regression for epidemiologists
Course number 2797
Program Epidemiologi
Language English
Credits 2.0
Notes The course meets the requirements for a general science course.

Date 2017-10-23 -- 2017-11-02
Responsible KI department Institutionen för folkhälsovetenskap
Specific entry requirements Knowledge in epidemiology and biostatistics equivalent to "Epidemiology I: Introduction to epidemiology" (course 1577) and "Biostatistics I: Introduction for epidemiologists" (course 1579) or corresponding courses
Purpose of the course This course focuses on the application of linear and logistic regression in the analysis of epidemiological studies.
Learning outcomes After successfully completing this course you as a student are expected to be able to:
- choose a suitable regression model for assessing a specific research hypothesis using data collected from an epidemiological study, fit the model using standard statistical software, evaluate the fit of the model, and interpret the results.
- explain the concept of confounding in epidemiological studies and demonstrate how to control/adjust for confounding using statistical models.
- apply and interpret appropriate statistical models for studying effect modification.
- critically evaluate the methodological aspects (design and analysis) of a scientific article reporting an epidemiological study.

Intended learning outcomes are classified according to Bloom¿s taxonomy: knowledge, comprehension, application, analysis, synthesis, and evaluation (Bloom, 1956, extended by Anderson and Krathwohl, 2001).
Contents of the course This course focuses on the application of linear and logistic regression in the analysis of epidemiological studies. Topics covered include a brief introduction to continuous and binary outcome data, univariable and multivariable models, interpretation of parameters for continuous and categorical predictors, flexible modeling of quantitative predictors, confounding and interaction, model fitting and model diagnostics.
Teaching and learning activities Lectures, computer lab with exercises focusing on analysis of real data sets using statistical software, exercises not requiring statistical software, group discussions, literature review.
Compulsory elements The individual written examination (summative assessment).
Examination To pass the course, the student has to show that the learning outcomes have been achieved. The course grade is based on the individual written examination (summative assessment). The focus of the examination will be on understanding concepts and their application to analysis of epidemiological studies rather than mathematical detail. Students who do not obtain a passing grade in the first examination will be offered a second examination within two months of the final day of the course. Students who do not obtain a passing grade at the first two examinations will be given top priority for admission the next time the course is offered. If the course is not offered during the following two academic terms then a third examination will be scheduled within 12 months of the final day of the course.
Literature and other teaching material Hosmer DW, Lemeshow S, and Sturdivant, RX. Applied Logistic Regression, 3rd Ed, A Wiley-Interscience Publication, John Wiley & Sons Inc., New York, NY, 2013.
Number of students 8 - 25
Selection of students Eligible doctoral students, with required prerequisite knowledge, will be selected based on 1) the relevance of the syllabus for the applicant's doctoral project (according to written motivation), and 2) date for registration as doctoral student (priority given to earlier registration date). To be considered, submit a completed application form. Give all information requested, including a description of current research and motivation for attending, and an account of previous courses taken.
More information The course is extended over time in order to promote reflection and reinforce learning. The course will be given the following dates: October 23, 25, 27, 30, November 1 and 2.
Additional course leader
Earlier evaluation of the course Evaluation report
Course responsible Nicola Orsini
Institutionen för folkhälsovetenskap

Nicola.Orsini@ki.se
Contact person Marita Larsson
Institutionen för folkhälsovetenskap
08-524 801 05
marita.larsson@ki.se